Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis

Multiscale methods have become progressively valuable in geomorphometric analysis as data increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, choice interpolator had a negligible impact relative to effects scale. LQR was not ideal for rigorous multiscale analyses due inherently non-linear processing time algorithm poor fit with surface. fGSS combined desirable identified optimal analysis. support efficacy general framework analyses.